Nox software sensor

ABSTRACT

A method and apparatus for predicting the NOx concentration in flue gas produced by combustion in a furnace. This prediction does not physically require any NOx sensors, and the only measurements required are inputs to a neural network. The required measurements are of combustion pressure, combustion or flue gas temperature, and some datum which is representative of the nitrogen concentration in the flue gas. This method and apparatus results in less expensive, immediate NOx value estimates, in contrast to more expensive, delayed measurements from physical NOx sensors. By limiting the number of input data to 5 or fewer, the noise resulting form too large a number of data is limited. This method and apparatus is applicable to furnaces fueled with fuel oil, natural gas, or a fuel oil and natural gas mixture, as well as to furnaces which use oxygen or oxygen-enriched air as an oxidant.

[0001] The invention relates to the field of the measurement of gasemissions of the NOx type, in particular in industrial processes.

[0002] Numerous industrial processes are based on the combustion ofvarious fuels such as natural gas, LPG etc. Among these processes may becited in particular second-melt smelting processes or those employed infurnaces for the melting of glass.

[0003] The oxidant for these processes, traditionally air, can beenriched with oxygen or even, in certain cases, replaced by oxygen.These processes produce in particular gases such as NOx compounds. Theformation of these compounds depends on numerous parameters, whichthemselves depend on the process (nature of the charge, composition ofthe oxidant and fuel fluids, burner, pressure of the furnace, etc.).

[0004] Ever stricter standards are imposed as regards concern for theenvironment.

[0005] Continuous measurement of these compounds would allow bettercontrol of the processes from which they emanate, and/or would make itpossible to minimize releases or emissions of NOx so as to comply as faras possible with the standards imposed.

[0006] The known methods of measurement can be grouped into twocategories: on the one hand in situ measurements, by an appropriate NOxphysical sensor and, on the other hand, measurements or estimates bysoftware sensor.

[0007] In the example of processes which are conducted in a furnace, thesmoke exiting the furnace is often at high temperatures (between 1400°C. and 1600° C.) and is laden with dust. All these conditions thereforeaffect the operation of the NOx physical sensors which may be installedat the exit of a furnace.

[0008] In order to reduce the overly high temperatures of the smoke,recourse is had to a gas conditioning procedure.

[0009] Furthermore, measurements made by a physical sensor requirefrequent calibration as well as technical monitoring of the sensor.

[0010] Physical sensors, which are in themselves expensive, aretherefore unsuitable for continuous tracking at reasonable cost.

[0011] Software sensors are also known, but they do not currently makeit possible to attain high accuracy.

[0012] In particular, a relative error of estimation of the order of 70%is obtained, which is too large for current needs.

[0013] Furthermore, such a sensor requires, in practice, a considerablenumber of inputs (around 14) and therefore utilizes this many physicalsensors to acquire these data. This results in considerable noise in theinput data.

[0014] Hence, the problem arises of finding a novel process and a noveldevice for measuring oxides of nitrogen (NOx) in smoke, especially atthe furnace exit.

[0015] The problem also arises of finding a process and a device givingreliable information regarding the concentration of oxides of nitrogen(NOx) in smoke without performing a direct physical measurement of theseoxides of nitrogen.

[0016] The problem also arises of finding a sensor allowing continuousmeasurement, in a reliable manner and with industry-acceptablemaintenance, of the emissions of NOx in smoke, in particular at thefurnace exit.

[0017] The problem furthermore arises of finding a sensor of softwaretype not requiring too large a number of input data.

DESCRIPTION OF THE INVENTION

[0018] The invention relates to a process for measuring the content ofNOx compounds (x=1 or 2) contained in smoke produced by combustion.

[0019] The invention relates to a process for measuring the NOx contentin the smoke produced by combustion in a furnace, characterized in that:

[0020] at least one datum of pressure in the furnace, at least one datumof temperature in the furnace and/or in the smoke resulting from thecombustion, and at least one datum representative of the concentrationof nitrogen in the smoke are measured as the combustion proceeds,

[0021] these data, or data processed or obtained from these data, areintroduced in the guise of input data for a neural network, whichdelivers at least one output datum representative of the concentrationor of the content of NOx in the smoke resulting from the combustion.

[0022] According to this process, the measurement does not require anyNOx physical sensor, the only measurements performed being those inrespect of the input data for the neural network, or else those fromwhich these input data are calculated.

[0023] Such a sensor employing a neural network requires neitherrecalibration nor maintenance.

[0024] The estimate of the NOx values is immediate as compared with themeasurement of an NOx physical sensor, and the implementation, of theprocess according to the invention is less expensive than that of aphysical sensor.

[0025] In the process proposed, only a minimum number of input data areused: a pressure measurement, a temperature measurement and one or twoconcentration data (carbon dioxide (CO2) and/or the concentration ofoxygen (O2), in the smoke) to obtain information regarding the quantityor concentration of nitrogen in the smoke.

[0026] The accuracy obtained is furthermore much better than thatobtained with the currently known software sensors.

[0027] By limiting the input data to 5 or fewer, the noise resultingfrom too large a number of data is also limited.

[0028] The fuel can be a natural gas or else fuel oil or a natural gasand fuel oil mixture.

[0029] The oxidant can be oxygen or oxygen-enriched air as oxidant.

[0030] According to a particular embodiment, at least two data ofpressure in the furnace are measured as the combustion proceeds, thesepressure data are processed so as to calculate the mean thereof, andthis mean pressure datum is introduced in the guise of input datum forthe neural network.

[0031] According to another aspect of the invention, at least two dataof temperature in the furnace are measured as the combustion proceeds,these temperature data are processed so as to calculate the meanthereof, and this mean temperature datum is introduced in the guise ofinput datum for the neural network.

[0032] Preferably, the measured data exhibit a degree of correlation,with NOx concentration or content data, greater than a predetermineddegree.

[0033] The processing of the data so as to deliver a datumrepresentative of the NOx content can be carried out continuously, thatis to say with a temporal periodicity of the order of a few seconds.

[0034] The invention also relates to a device for measuring the NOxcontent in the smoke produced by combustion in a furnace, characterizedin that it includes:

[0035] sensors for measuring at least one datum of pressure in thefurnace, at least one datum of temperature in the furnace and/or in thesmoke resulting from the combustion, and at least one datumrepresentative of the concentration of nitrogen in the smoke,

[0036] means for or programmed for:

[0037] having the said data processed by a neural network, or forprocessing at least part of these data to form input data for a neuralnetwork and for processing the said data processed by a neural network,

[0038] and for delivering at least one output datum representative ofthe NOx concentration or content in the smoke resulting from thecombustion.

[0039] The invention also relates to a combustion system including aburner, a furnace, means for discharging combustion products, and ameasurement device as above.

[0040] The furnace is for example a glass furnace, or a second-meltrotary smelting furnace, or an incineration furnace.

[0041] The invention also relates to a computer program comprisinginstructions for processing, according to a neural network, at least onedatum of pressure of a furnace, at least one datum of temperature in thesame furnace and/or in the smoke resulting from combustion occurring inthe said furnace, and at least one datum representative of theconcentration of nitrogen in the smoke, and for calculating, accordingto this neural network, at least one output datum representative of theNOx concentration or content in the smoke resulting from the combustion.

[0042] The invention also relates to a computer program comprisinginstructions for:

[0043] processing at least part of the data from at least one datum ofpressure of a furnace, at least one datum of temperature in the samefurnace and/or in the smoke resulting from combustion occurring in thesaid furnace, and at least one datum representative of the concentrationof nitrogen in the smoke, and for forming input data for a neuralnetwork (32),

[0044] calculating, according to this neural network, at least oneoutput datum representative of the NOx concentration or content in thesmoke resulting from the combustion.

BRIEF DESCRIPTION OF THE FIGURES

[0045] The characteristics and advantages of the invention will be moreapparent in the light of the description which follows. This descriptionpertains to the exemplary embodiments, given by way of non-limitingexplanation, whilst referring to the appended drawings in which:

[0046]FIG. 1 represents a neural network,

[0047]FIG. 2 represents a basic diagram of the creation of a neuralnetwork,

[0048]FIG. 3 represents a furnace structure,

[0049]FIG. 4 represents the neural network of a sensor according to theinvention,

[0050]FIGS. 5 and 6 represent data acquisition and processing meanswhich can be used within the framework of the present invention,

[0051]FIG. 7 represents comparative results obtained with the aid of twosensors, one of which is according to the invention.

DETAILED DESCRIPTION OF EMBODIMENTS OF THE INVENTION

[0052] The invention employs a neural network to carry out a measurementor an estimation of the quantities of oxides of nitrogen produced by acombustion.

[0053] A neural network 32 is represented diagrammatically in FIG. 1.The references 20, 22, 24, 26 designate various network layers,including an input layer 20, an output layer 26 and various hiddenlayers 22, 24.

[0054] In FIG. 1 only two hidden layers are represented, but the networkmay also comprise just one, or more than two. In the case of the presentinvention, the output layer 26 delivers the quantity of NOx to the user.

[0055] Each layer k comprises a certain number of synapses ik (N1 forlayer 20, N2 for layer 22, N3 for layer 24, and 2 for the output layer26).

[0056] The input data (processed data) are introduced into the synapsesof the input layer.

[0057] To each synapse of the neural network there corresponds anonlinear activation function F, such as a hyperbolic tangent functionor a sigmoid function, as well as an activation level.

[0058] Moreover, each synapse i of each layer is linked to the synapsesj of the next layer, and a weighting Pij weights each link between asynapse i and a synapse j.

[0059] This weighting weights the influence of the result of eachsynapse i in the calculation of the result delivered by each synapse jto which it is linked.

[0060] The output Sj from a synapse j is equal to the value of theactivation function Fj applied to the weighted sum, by the weights Pijof the synapses, of the results Ai of the activation functions of thesynapses i which are connected to it. Stated otherwise:S_(j) = F_(j)( _(i)^(S)Pij × Ai).

[0061] The network 32 represented in FIG. 1 is an open network. In alooped network, one of the output data is reused as input datum.

[0062] The work entitled “Modélisation, Classification et Commande parRéseaux de Neurones”: Méthodologie de Conception et IllustrationsIndustrielles” [Modelling, Classification and Control using NeuralNetworks Design Methodology and Industrial Illustrations] by I. Rivals,L. Personnaz, G. Dreyfus, J. L. Ploix (Les Réseaux de Neurones pour laModélisation et la Commande de Procédés [Neural Networks for ProcessModelling and Control], J. P. Corriou, coordinator, Lavoisier Tec et Doc1995) gives additional information about neural networks.

[0063] Physical data, relating to a combustion process which producesNOx compounds, can be measured as such a process is being conducted;these data are then input to the network.

[0064] They may be processed beforehand so as to ascertain whether theyare relevant, preferably non-redundant, and provide a preferablycomplete determination of the process.

[0065] Relevance means that the information contained in each of theinput data contributes to the formation of the result (the NOx content).The mathematical form of this contribution need not be known in order toobtain this result.

[0066] Non-redundancy means that the correlations between the selectedinputs are small.

[0067] Completeness means that all the information required to produce aneural network is present in the suite of data.

[0068] The processing of the data with a view to creating a neuralnetwork involves acquiring raw or physical data regarding the process.This acquisition preferably covers the entire range over which onewishes the system to be able to predict.

[0069] Also, and in accordance with the diagram of FIG. 2, raw data arefirstly measured (step 29).

[0070] These data are then introduced into a piece of software 30 forcreating neural networks.

[0071] In this software, the majority of the data is used for thelearning phase 34, the remainder of the data contributing to thevalidation of the network (step 36).

[0072] The final choice of the best network depends in fact on theobjectives fixed. One seeks in fact to obtain the smallest relativedeviation, or a predetermined relative deviation, between themeasurements (of NOx, these measurements being carried out with the aidof NOx sensors) and the predictions of the network.

[0073] Various data processing tools can furthermore be used.

[0074] Thus, in the preprocessing phase, it is possible to average or tofilter the data with the aim of reducing the acquisition noise.

[0075] In the processing phase, a matrix of covariance of the potentialinputs for the network can be used so as to limit the redundant dataaffording the same information.

[0076] The input/output correlation vectors can also be used todetermine the most influential inputs for the prediction of the saidoutput.

[0077] According to the invention, a suite of relevant, non-redundantdata ensuring the completeness of the system is the following:

[0078] at least one pressure measured in the furnace,

[0079] at least one temperature measured in the furnace or in the smoke,

[0080] at least one datum representative of the concentration orquantity of nitrogen in the smoke.

[0081] By way of example, we may take:

[0082] at least one pressure in the furnace,

[0083] the temperature of the smoke,

[0084] the concentration of CO2 in the smoke,

[0085] the concentration of O2 in the smoke.

[0086] The quantity of nitrogen in the smoke can be deduced from theconcentration of CO2 and of O2 in the smoke:

[N2]=1−[CO2]−[O2].

[0087] All these data are obtained by measurements with the aid ofhardware sensors (pressure sensors, temperature sensors, sensors formeasuring concentrations in the smoke).

[0088] Measurements of NOx can be obtained with an NOx hardware sensorfor a certain duration and with monitoring of an operator.

[0089] These raw data are applied to the input layer 20 of the neuralnetwork 32, the whole constituting a data set which is sufficient toproduce a neural network which can be used in continuous mode.

[0090] It is also possible to use other additional data, in particular,so as to increase the accuracy of the result or to increase the rate ofconvergence of the neural network.

[0091] It is for example possible to use a mean pressure in the furnace,instead of a single pressure, this mean resulting from several pressuredata obtained by several sensors.

[0092] Likewise, it is possible to use a mean temperature in thefurnace, instead of or in addition to the temperature in the smoke. Thismean temperature then results from several temperature data obtained byseveral sensors disposed in the furnace.

[0093] The network itself can be obtained by implementing neural networkproduction software, such as the NeuroOne software, from the NETRALcompany.

[0094] The work by I. RIVALS et al. already cited hereinabove gives allthe indications for constructing such a neural network. Reference mayalso be made to the thesis by I. RIVALS, Université Pierre et MarieCurie, 1995, entitled “Modélisation et commande de processus par réseauxde neurones; application au pilotage d'un véhicule autonome” [Processmodelling and control by neural networks; application to the steering ofan autonomous vehicle].

[0095] The user or the designer of the network indicates the followingdata to this algorithm or to the software used:

[0096] number of hidden layers desired,

[0097] the form of desired activation function (hyperbolic tangent orsigmoid),

[0098] the choice of a looped or open network,

[0099] the input data, and corresponding measurements of NOx. Thesemeasurements are obtained with an NOx hardware sensor disposed at thestack exit for a certain duration and with monitoring of an operator.Once the network has been constructed and tailored, this NOx hardwaresensor is no longer used.

[0100] With these data, the software or the algorithm determines thesynapses of the neural network and the corresponding weights. Moreprecisely, software is produced in source code or in executable code,which enables the user to obtain NOx concentration data as a function ofphysical data or raw data measured directly on the process.

[0101] If this measurement of raw data is carried out in a continuous oralmost continuous manner. (that is to say with a period of the order ofa few seconds, for example 1 to 5 or to 15 seconds or with a frequencyof between 1 Hz and 0.01 Hz), the sensor thus constructed can deliver,continuously or quasi-continuously (with the same period or frequency),a measurement or a signal representative of a measurement of the NOxcontent produced.

[0102] Preferably, for the application to NOx measurements, one choosesa neural network:

[0103] with a single hidden layer, the calculation times for a networkwith two hidden layers or more being too large in the case of a desiredperiod of use of around a few seconds, for example 1 to 5 or to 15seconds,

[0104] static, with no loop.

[0105] By way of example, the data processing and the specific modellingof a furnace will be given. This furnace uses pure oxygen as oxidant,natural gas as fuel, and is equipped with a 1 MW burner.

[0106] Its structure is given diagrammatically in FIG. 3, in which thereference 40 designates the burner itself, supplied via pipes 42 and 44with fuel and oxidant respectively, and the reference 46 the furnace inwhich the combustion occurs.

[0107] A stack 48 is disposed at the exit of the furnace 46, the openingof a damper 50 making it possible to regulate the pressure in thefurnace.

[0108] A water circuit system (not represented in the figure) enablesenergy to be transferred to a charge.

[0109] Thermocouples, disposed against the wall of the furnace, on theoutside, make it possible to measure the outside temperature of thefurnace.

[0110] Temperature sensors are disposed in the roof, on the inside ofthe furnace 46. For example 11 sensors (only two of which arerepresented) are disposed along the roof, from the entrance of thefurnace up to its exit.

[0111] Thus, a sensor 54 makes it possible to measure the rooftemperature, in proximity to the root of the flame 52, whilst a sensor56 makes it possible to measure the roof temperature, in proximity tothe exit of the furnace 46.

[0112] Two pressure sensors 55, 57 are also disposed in the furnace.

[0113] A temperature sensor 58 can furthermore be disposed in the stack48, so as to measure the temperature of the smoke. Likewise, sensors 60,62 make it possible to measure concentrations of CO₂ and of oxygen(preferably dry).

[0114] A neural network for such a furnace can be constructed with theaid of the NeuroOne software from the NETRAL company. The network istherefore delivered in the form of an executable code.

[0115] The physical data measured or the raw data used are: the 2pressures of the furnace (measured with the aid of the sensors 55 and57), the percentages of CO2 and of oxygen in the smoke (measured withthe aid of the sensor 62), the roof temperatures measured longitudinallyin the furnace, the percentage of nitrogen in the fuel, the purity ofthe oxygen used, the flow rate of oxygen introduced via the pipe 44, thetemperature of the smoke (measured with the sensor 58), and the flowrate of fuel introduced via the pipe 42.

[0116] Processing of the data makes it possible to:

[0117] calculate the mean of the pressures delivered by the sensors 55,57,

[0118] calculate the mean temperature in the furnace, from thetemperatures measured by each of the 11 sensors 54, 56.

[0119] The 5 data input to the neural network are then:

[0120] the mean pressure in the furnace,

[0121] the temperature of the smoke,

[0122] the concentration of CO2 in the smoke,

[0123] the concentration of O2 in the smoke,

[0124] the mean temperature in the furnace.

[0125] In fact each of these data is preferably considered as an averageover a certain time interval, for example as a moving average over aninterval of 3 minutes, with an acquisition period which may be 15 s.

[0126] A bias is further generated by the software for constructing theneural network.

[0127] The network output is preferably thresheld, that is to say theNOx concentrations which are below a certain threshold or below acertain predetermined limit value, for example 200 ppm, are disregarded.This is because an NOx value below such a threshold may correspond to adeficiency of one of the sensors and hence be of no interest in themodelling.

[0128] The structure of the network obtained is representeddiagrammatically in FIG. 4. The network comprises just one hidden layer22. It furthermore comprises the input layer 20 and output layer 26, thereference 21 designating the input bias.

[0129] The data are averaged over time, as indicated by the symbol < . .. >. The subscript f relates to the data measured in the furnace. Thosefor which an average has been produced between several sensors have abar above them.

[0130] A system for processing the measurements performed is representedin FIGS. 5 and 6.

[0131] Such a system comprises a microcomputer PC 70 to which the datameasured by the sensors 54-62 are transmitted via a link 61.

[0132] More precisely, the microcomputer 70 comprises (FIG. 6) amicroprocessor 82, a set of RAM memories 80 (for storing data), a ROMmemory 84 (for storing program instructions).

[0133] A data acquisition card 89 transforms the analogue data deliveredby the sensors into digital data and puts these data into the formatrequired by the microcomputer. These various elements are linked to abus 88.

[0134] Peripheral devices (screen or display device 74, mouse 90) allowinteractive dialogue with a user. In particular, the display means(screen) 74 make it possible to provide a user with a visual indicationrelating to the calculated NOx content.

[0135] Optionally, a link 63 makes it possible to modify certainoperating parameters of a combustion process.

[0136] Loaded into the microcomputer 70 are the data or the instructionsfor implementing a processing of the raw or physical data according tothe invention, and in particular for performing the prior processing 30of the raw or directly measured data (see FIG. 2), and for calculatingthe NOx content with the aid of a neural network 32.

[0137] These data or instructions for processing the raw or physicaldata can be transferred into a memory area of the microcomputer 70 froma diskette or any other medium which can be read by a microcomputer or acomputer (for example: hard disk, ROM read only memory, DRAM dynamicrandom access memory or any other type of RAM memory, compact opticaldisk, magnetic or optical storage element).

[0138] Comparative results are shown in FIG. 7. Curve I represents theresults obtained by modelling and curve II those obtained by measurementwith NOx sensors disposed directly in the stack 48 and constantlymonitored. As may be noted, modelling allows the best possibleapproximation to the NOx content since a standard deviation in therelative error of less than 2% between the calculated concentrations andthose measured by a physical sensor is obtained. Stated otherwise,95.45% of the NOx values predicted by the software sensor lie ±4% fromthe measured value.

[0139] Table I gives the standard deviations of the errors in the NOxconcentrations obtained with regard to the learning and validation data.TABLE I Learning Data Validation Data Relative Relative StandardStandard Standard Standard Type of network Deviation Deviation DeviationDeviation 5 inputs and 52 ppm 1.69% 54 ppm 1.79% 5 hidden neurons

[0140] The 5 inputs indicated in this example remain valid for othertypes of burners.

[0141] More generally, the software sensor according to the invention isadaptable to all types of furnaces using oxygen-enriched air as oxidantor pure oxygen.

[0142] Furthermore, the invention is independent of the control systemand accommodates all computer languages, thereby enabling it to beintegrated into any control system for existing industrial combustionprocesses.

[0143] An NOx measurement carried out in accordance with the inventioncan be used in monitoring mode, for example to trigger an alarm as soonas the NOx content oversteps a certain threshold.

[0144] It can also be used in a loop for regulating the input parametersof the monitored process. For example, all the input parameters arefixed, bar one, and the nonfixed parameter is regulated in such a way asto maintain the NOx content at a constant value or one lying between twovalues defining a range of variation.

[0145] Regulation is performed for example with the aid of the link 63(see FIG. 5) which transmits the regulating command to the process.

[0146] The invention has numerous fields of use. The invention appliesin particular to glass furnaces, to second-melt rotary smeltingfurnaces, to incineration furnaces, to chemical reactors requiring thepresence of a flame and whose oxidant is oxygen-enriched air.

[0147] According to the invention, a static model is thereforeimplemented in order to calculate the NOx emissions in variousindustrial processes, and in particular in the smoke from furnacesusing, in the guise of oxidant, oxygen-enriched air or oxygen.

1-21. (canceled).
 22. A process for determining the NOx content in fluegas produced by combustion in a furnace, comprising: a) measuring atleast one first parameter during the combustion process, said firstparameter comprising datum of pressure in said furnace; b) measuring atleast one second parameter during the combustion process, said secondparameter comprising datum of temperature, said temperature datum beingmeasured in the furnace, in the flue gas, or in both the furnace and theflue gas; c) measuring at least one third parameter during thecombustion process, said third parameter comprising datum representativeof the concentration of nitrogen in the flue gas; and d) inputtinginformation into a neural network which delivers at least one outputdatum representative of the concentration of NOx in the flue gasresulting from said combustion process, wherein at least one of saidinput information is selected from the group consisting: i) said firstparameter data; ii) said second parameter data; iii) said thirdparameter data; iv) data processed from said data; and v) data obtainedfrom these data.
 23. The process according to claim 22, wherein saidcombustion employs a fuel and an oxidant which comprises oxygen oroxygen-enriched air.
 24. The process according to claim 23, wherein saidfuel comprises natural gas, fuel oil, or a mixture of natural gas andfuel oil.
 25. The process according to claim 22, wherein at least twofirst parameter data are measured during the combustion process, themean value of said first parameter data is calculated, and said meanfirst parameter value is input as information into said neural network.26. The process according to claim 22, wherein at least two secondparameter data are measured during the combustion process, the meanvalue of said second parameter data is calculated, and said mean secondparameter value is input as information into said neural network. 27.The process according to claim 23, wherein said third parameter datacomprises the measurement of the concentration of carbon dioxide in theflue gas and the concentration of oxygen in said oxidant.
 28. Theprocess according to claim 22, wherein said input information for saidneural network comprise fewer than 5 data.
 29. The process according toclaim 22, wherein said input information exhibit a degree of correlationwith said NOx content.
 30. The process according to claim 29, whereinsaid degree of correlation is greater than a predetermined thresholdvalue.
 31. The process according to claim 22, wherein said neuralnetwork comprises a single hidden layer.
 32. The process according toclaim 22, wherein said neural network is static.
 33. The processaccording to claim 22, wherein said output of said neural networkcomprises a threshold.
 34. The process according to claim 22, whereinthe processing of said input information and the delivery of said outputdata is carried out periodically at a frequency of between about 1 andabout 0.01 Hz.
 35. An apparatus for determining the NOx content in fluegas produced by combustion in a furnace, comprising: a) sensors formeasuring at least one first parameter during the combustion process,said first parameter comprising datum of pressure in the furnace; b)sensors for measuring at least one second parameter during thecombustion process, said second parameter comprising datum oftemperature, said temperature datum being measured in the furnace, inthe flue gas, or in both the furnace and the flue gas; c) sensors formeasuring at least one third parameter during the combustion process,said third parameter comprising datum representative of theconcentration of nitrogen in the flue gas; d) means for having at leastpart of said first parameter data, said second parameter data, and saidthird parameter data processed by a neural network to form input datafor a neural network; and e) means for delivering at least one outputdatum representative of the NOx concentration in the flue gas.
 36. Acombustion system comprising a burner, a furnace, means for dischargingcombustion products, and a measurement device according to claim
 35. 37.The combustion system according to claim 36, wherein said furnace is aglass furnace, a second-melt rotary smelting furnace, or an incinerationfurnace.
 38. A computer program comprising: a) instructions forprocessing, according to a neural network, i) at least one datum ofpressure of a furnace, ii) at least one datum of temperature, saidtemperature datum being measured in the furnace, in the flue gas, or inboth the furnace and the flue gas, and iii) at least one datumrepresentative of the concentration of nitrogen in the flue gas; and b)instructions for calculating, according to this neural network, at leastone output datum representative of the NOx concentration in the fluegas.
 39. A computer program comprising; a) instructions for processingat least part of the data from: i) at least one datum of pressure of afurnace, ii) at least one datum of temperature, said temperature datumbeing measured in the furnace, in the flue gas, or in both the furnaceand the flue gas, and iii) at least one datum representative of theconcentration of nitrogen in the flue gas; b) instructions for forminginput data for a neural network; and c) instructions for calculating,according to this neural network, at least one output datumrepresentative of the NOx concentration in the flue gas.
 40. Thecomputer program according to claim 38, wherein said neural networkincludes a single hidden layer.
 41. The program according to claim 38,wherein said neural network is static.
 42. The program according toclaim 38, wherein the output of said neural network includes athreshold.
 43. The data medium, capable of being read by a computer,comprising coded instructions for a program according to claim 38.